In [1]:
#imports
import numpy as np
import pandas as pd
import datetime
import random
import math
from pandas import ExcelWriter
from pandas_profiling import ProfileReport
#load the Titanic data into a dataframe
titanic_excel = 'titanicData.xlsx'
titanicDF = pd.read_excel(titanic_excel)
Introduction¶
Welcome to Pandas Profiling! Pandas Profiling is a powerful tool to quickly display and summarize Panda datafames in Python. This tutorial notebook will walk you through with examples the many ways to use Pandas Profiling.
The dataset¶
For this tutorial, a dataset from Frank E. Harrell Jr. and Thomas Cason of Vanderbilt University is used. This dataset (from now on refered to as "titanicDF") contains information on 1,309 passengers who were onboard the Titanic when it sink on April 15, 1912.
The following is a short description of each variable in titanicDF:
| Variable Name | Description | Key |
|---|---|---|
| pclass | passenger class | 1=1st, 2=2nd, 3=3rd |
| survived | whether survived | 0=no, 1=yes |
| name | the name | |
| sex | the sex | |
| age | the age in years | |
| sibsp | # of siblings or spouses aboard the Titanic | |
| parch | # of parents / children aboard the Titanic | |
| fare | passenger fare | |
| cabin | cabin number | |
| embarked | port of embarkation | C = Cherbourg, Q = Queenstown, S = Southampton |
| body | body identification number | |
| home.dest | home/destination |
Generating a Report¶
Pandas Profiling is used to generate reports using a Pandas dataframe. After declaring a Pandas dataframe, a report can be generated by first importing ProfileReport:
In [5]:
from pandas_profiling import ProfileReport
Then, run the function ProfileReport, inputting your dataframe and a title as arguments:
In [12]:
titanicReport = ProfileReport(titanicDF, title='Titanic Report')
titanicReport
Out[12]:
And that's it! Take some time to explore the different menus in report, which are explained in greater detail in the following section.
Overview of Report Contents¶
A Pandas Profiling Report contains 6 sections, which can be navigated to by clicking on any of the 6 titles in the top-right of the report as shown here:
Each of these sections is described below:
Overview¶
The overview section has three tabs: "Overview", "Warnings", and "Reproduction". Overview provides a quick snapshot of the data. Warnings alerts you to any potential issues with the data, such as if a column contains a large number of missing values. Reproduction details information on when the report was created and in what version of Pandas Profiling.
Variables¶
The variables section of the report analyzes each variable in the dataset. To the right of each variable name, information is displayed detailing the number of distinct values the variable can take, this number divided by the number of entries for the variable (so in the case above, Distinct(%) = 3/1309), and information on the number and percent of missing values.
Clicking "toggle details" in the lower right displays more information and visualizations for the variable. The information displayed will vary depending on the type of variable, such as if it is categorical or numeric.
Interactions¶
The Interaction section allows you to plot a scatterplot of any two numeric variables in the dataset. The two variables being plotted are selected in the two rows at the top of the section. The x-axis will be the first selection, and the y-axis will be the second selection.
Correlations¶
The correlation section plots the correlations between all possible variables in the dataset. The 4 options at the top allow you to toggle between types of correlations, and the "toggle correlation descriptions" button displays the description for the currently selected correlation type. In the graph, a darker shade of blue corresponds to a stronger positive correlation, and a darker shade of red corresponds to a stronger negative correlation.
Missing values¶
The Missing Values section details information about the number and intensity of missing values in the dataset.
The "Count" tab displays the number of filled entries for each variable. The "Matrix" tab displays where the missing values occur in the data by arranging the data by entry number and coloring-in all but the missing values. For example:
In the above Matrix, for the cabin variable we can see that only about the first third of the entries have any filled values, while the latter two-thirds are missing.
The "Heatmap" tab displays any correlations between the missing values of different variables. "Dendrogram" groups together variables with similar amounts of missing values.
Samples¶
The Samples section displays the first and last 10 rows of the dataset.
Customization¶
A Pandas Profiling report can be customized to better suit your needs. The documentation provides a good detailed list of all the different changes that can be made to the report. Most parts of the report can be removed, and other general changes can be made such as whether to display a loading progress bar. Additionally, different warning thresholds can be set that alert the user in the Overview section of the report.
This changes can be made by adjusting the arguements when calling the ProfileReport function. For example, if you wanted to generate a report that excludes the Correlations and Missing Values sections, the following code could be run:
In [19]:
titanicReport2 = ProfileReport(titanicDF, title='Titanic Report Updated', missing_diagrams= None, correlations = None)
titanicReport2
Out[19]:
Widget¶
Another way to customize your report is to display it as a widget in a Jupyter Notebook. This can be achieved by adding ".to_widgets()" after the report variable, as in the below example.
First, install Jupyter widgets:
In [35]:
!pip install jupyter_contrib_nbextensions
!jupyter nbextension enable --py widgetsnbextension
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Enabling notebook extension jupyter-js-widgets/extension...
- Validating: ok
Then run the report with ".to_widgets()":
In [25]:
titanicReport = ProfileReport(titanicDF, title='Titanic Report')
titanicReport.to_widgets()
Saving the Report¶
A Pandas Profiling Report can be saved as an html file. To do so, run the function "to_file()" on your report with the desired name of your html file inside the parentheses, as in the example below.
First generate a report:
In [2]:
titanicReport = ProfileReport(titanicDF, title='Titanic Report')
Then run the function "to_file()":
In [3]:
titanicReport.to_file("titanicFile.html")
The html file "titanicFile" should now be saved inside the folder that contains this Jupyter Notebook!
You can also convert the report to a json in a similar manner, by calling the function "to_json()" on your report. A json report can be saved in your home directory by calling "to_file()" and naming it a .json file.
In [32]:
# As a string
titanic_json = titanicReport.to_json()
#As a file
titanicReport.to_file("titanic_report.json")
In [30]:
json_data
Out[30]:
'{\n "analysis": {\n "title": "Titanic Report",\n "date_start": "2020-11-06 13:50:52.093508",\n "date_end": "2020-11-06 13:50:56.024391",\n "duration": "0:00:03.930883"\n },\n "table": {\n "n": 1309,\n "n_var": 12,\n "memory_size": 470615,\n "record_size": 359.5225362872422,\n "n_cells_missing": 3032,\n "n_vars_with_missing": 6,\n "n_vars_all_missing": 0,\n "p_cells_missing": 0.1930226636108989,\n "n_duplicates": 0,\n "p_duplicates": 0.0,\n "CAT": 0,\n "BOOL": 0,\n "NUM": 0,\n "DATE": 0,\n "URL": 0,\n "COMPLEX": 0,\n "PATH": 0,\n "FILE": 0,\n "IMAGE": 0,\n "UNSUPPORTED": 0,\n "types": {\n "CAT": 6,\n "NUM": 5,\n "BOOL": 1\n }\n },\n "variables": {\n "pclass": {\n "value_counts": {\n "3": "709",\n "1": "323",\n "2": "277"\n },\n "distinct_count_with_nan": 3,\n "distinct_count_without_nan": 3,\n "type": "Variable.TYPE_CAT",\n "n": 1309,\n "count": 1309,\n "n_distinct": 3,\n "p_distinct": 0.002291825821237586,\n "p_missing": 0.0,\n "n_missing": 0,\n "is_unique": "False",\n "n_unique": 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59.86\\n 66.4 72.94 79.48 86.02 92.56 99.1 105.64 112.18 118.72 125.26\\n 131.8 138.34 144.88 151.42 157.96 164.5 171.04 177.58 184.12 190.66\\n 197.2 203.74 210.28 216.82 223.36 229.9 236.44 242.98 249.52 256.06\\n 262.6 269.14 275.68 282.22 288.76 295.3 301.84 308.38 314.92 321.46\\n 328. ]"\n ]\n },\n "home.dest": {\n "value_counts": {\n "New York, NY": "64",\n "London": "14",\n "Montreal, PQ": "10",\n "Paris, France": "9",\n "Cornwall / Akron, OH": "9",\n "Philadelphia, PA": "8",\n "Wiltshire, England Niagara Falls, NY": "8",\n "Winnipeg, MB": "8",\n "Belfast": "7",\n "Brooklyn, NY": "7",\n "Sweden Winnipeg, MN": "7",\n "Bulgaria Chicago, IL": "5",\n "Ottawa, ON": "5",\n "Somerset / Bernardsville, NJ": "5",\n "Rotherfield, Sussex, England Essex Co, MA": "5",\n "Sweden Worcester, MA": "5",\n "Haverford, PA / Cooperstown, NY": "5",\n "Minneapolis, MN": "4",\n "Montreal, PQ / Chesterville, ON": "4",\n "Guntur, India / Benton Harbour, MI": "4",\n "Haverford, PA": "4",\n "Washington, DC": "4",\n "Bryn Mawr, PA": "4",\n "Youngstown, OH": "4",\n "Devon, England Wichita, KS": "4",\n "London, England": "4",\n "Paris / Haiti": "4",\n "Syria New York, NY": "4",\n "Ruotsinphytaa, Finland New York, NY": "4",\n "San Francisco, CA": "4",\n "Ireland New York, NY": "4",\n "St Louis, MO": "4",\n "Ireland Chicago, IL": "4",\n "Austria": "4",\n "Guernsey / Elizabeth, NJ": "4",\n "Chicago, IL": "4",\n "Paris / Montreal, PQ": "4",\n "Bournmouth, England": "4",\n "Elkins Park, PA": "3",\n "Paris": "3",\n "Bishopstoke, Hants / Fayette Valley, ID": "3",\n "Hudson, NY": "3",\n "Bangkok, Thailand / Roseville, IL": "3",\n "Strood, Kent, England Detroit, MI": "3",\n "Hong Kong New York, NY": "3",\n "Syria": "3",\n "Newark, NJ": "3",\n "Lexington, MA": "3",\n "Portland, OR": "3",\n "England Brooklyn, NY": "3",\n "Duluth, MN": "3",\n "Ilford, Essex / Winnipeg, MB": "3",\n "Milwaukee, WI": "3",\n "Nice, France": "3",\n "Greenport, NY": "3",\n "Zurich, Switzerland": "3",\n "Cape Town, South Africa / Seattle, WA": "3",\n "Syria Kent, ON": "3",\n "Boston, MA": "3",\n "Montevideo, Uruguay": "3",\n "Stanton, IA": "3",\n "Tuxedo Park, NY": "3",\n "Plymouth, Devon / Detroit, MI": "3",\n "East Providence, RI": "3",\n "Los Angeles, CA": "3",\n "Rochester, NY": "3",\n "Southampton": "3",\n "Ireland": "3",\n "Lakewood, NJ": "3",\n "West Hampstead, London / Neepawa, MB": "3",\n "West Bromwich, England Pontiac, MI": "3",\n "Syria Ottawa, ON": "3",\n "Sweden Worcester, MA": "3",\n "Bromsgrove, England / Montreal, PQ": "2",\n "Cornwall": "2",\n "St Ives, Cornwall / Hancock, MI": "2",\n "Warwick, England": "2",\n "Geneva, Switzerland / Radnor, PA": "2",\n "Guernsey": "2",\n "Basel, Switzerland": "2",\n "Scituate, MA": "2",\n "Co Athlone, Ireland New York, NY": "2",\n "Moscow / Bronx, NY": "2",\n "Cornwall / Spokane, WA": "2",\n "Cornwall / Camden, NJ": "2",\n "Deephaven, MN / Cedar Rapids, IA": "2",\n "Mt Airy, Philadelphia, PA": "2",\n "England Albion, NY": "2",\n "Aberdeen / Portland, OR": "2",\n "London / Paris": "2",\n "Paris, France / New York, NY": "2",\n "Denmark Hill, Surrey / Chicago": "2",\n "Bournemouth, England Newark, NJ": "2",\n "Norwich / New York, NY": "2",\n "Bristol, England / New Britain, CT": "2",\n "New York, NY / Stamford CT": "2",\n "New Forest, England": "2",\n "Indianapolis, IN": "2",\n "England": "2",\n "Calgary, AB": "2",\n "Barcelona, Spain / Havana, Cuba": "2",\n "England / San Francisco, CA": "2",\n "Swindon, England": "2",\n "Providence, RI": "2",\n "Cornwall / Hancock, MI": "2",\n "Plymouth, England": "2",\n "Altdorf, Switzerland": "2",\n "Worcester, MA": "2",\n "Little Onn Hall, Staffs": "2",\n "London / East Orange, NJ": "2",\n "Dowagiac, MI": "2",\n "Greenwich, CT": "2",\n "Detroit, MI": "2",\n "Liverpool": "2",\n "Madrid, Spain": "2",\n "West Haven, CT": "2",\n "Co Longford, Ireland New York, NY": "2",\n "Huntington, WV": "2",\n "Weston-Super-Mare, Somerset": "2",\n "Greece": "2",\n "Bronx, NY": "2",\n "Russia New York, NY": "2",\n "Penzance, Cornwall / Akron, OH": "2",\n "England / Sacramento, CA": "2",\n "Portugal": "2",\n "Skara, Sweden / Rockford, IL": "2",\n "Ilfracombe, Devon": "2",\n "Amenia, ND": "2",\n "England / Bennington, VT": "2",\n "Stockholm, Sweden": "2",\n "Spain / Havana, Cuba": "2",\n "Vancouver, BC": "2",\n "St Ives, Cornwall / Calumet, MI": "2",\n "Trenton, NJ": "2",\n "Halifax, NS": "2",\n "Lucca, Italy / California": "2",\n "Liverpool / Montreal, PQ": "2",\n "London, England Norfolk, VA": "2",\n "Cornwall / Houghton, MI": "2",\n "Liverpool, England Bedford, OH": "2",\n "Mamaroneck, NY": "2",\n "St Leonards-on-Sea, England Ohio": "2",\n "Fond du Lac, WI": "2",\n "New York, NY / Ithaca, NY": "2",\n "Bridgerule, Devon": "2",\n "Elizabeth, NJ": "2",\n "Cornwall / Clear Creek, CO": "2",\n "Southington / Noank, CT": "2",\n "London / New York, NY": "2",\n "Tampico, MT": "2",\n "Brunswick, ME": "2",\n "Isleworth, England": "2",\n "Croatia": "1",\n "Brockton, MA": "1",\n "Columbus, OH": "1",\n "West Kensington, London": "1",\n "Westcliff-on-Sea, Essex": "1",\n "London Vancouver, BC": "1",\n "Seattle, WA / Toledo, OH": "1",\n "Krakudden, Sweden Moune, IL": "1",\n "Merrill, WI": "1",\n "East Orange, NJ": "1",\n "Glen Ridge, NJ": "1",\n "Barre, Co Washington, VT": "1",\n "Milford, NH": "1",\n "St Denys, Southampton, Hants": "1",\n "Austria-Hungary": "1",\n "Bryn Mawr, PA, USA": "1",\n "Clevedon, England": "1",\n "Belgium Detroit, MI": "1",\n "St Austall, Cornwall": "1",\n "Sweden Akeley, MN": "1",\n "St James, Long Island, NY": "1",\n "Effington Rut, SD": "1",\n "Omaha, NE": "1",\n "Guernsey / Montclair, NJ and/or Toledo, Ohio": "1",\n "Harrisburg, PA": "1",\n "Hornsey, England": "1",\n "Holley, NY": "1",\n "Austria-Hungary / Germantown, Philadelphia, PA": "1",\n "Liverpool, England / Belfast": "1",\n "Winnipeg, MN": "1",\n "Portugal / Sau Paulo, Brazil": "1",\n "Brighton, Sussex": "1",\n "Perkins County, SD": "1",\n "London Brooklyn, NY": "1",\n "Weston-Super-Mare / Moose Jaw, SK": "1",\n "Treherbert, Cardiff, Wales": "1",\n "Deer Lodge, MT": "1",\n "West Hoboken, NJ": "1",\n "England Oglesby, IL": "1",\n "Seattle, WA": "1",\n "Gallipolis, Ohio / ? Paris / New York": "1",\n "New York, NY / Greenwich CT": "1",\n "Spain": "1",\n "England Salt Lake City, Utah": "1",\n "Co Sligo, Ireland New York, NY": "1",\n "Pennsylvania": "1",\n "Glasgow / Bangor, ME": "1",\n "Bulgaria Coon Rapids, IA": "1",\n "Co Sligo, Ireland Hartford, CT": "1",\n "Victoria, BC": "1",\n "Paris / New York, NY": "1",\n "Janjgir, India / Pennsylvania": "1",\n "Brookline, MA": "1",\n "London New York, NY": "1",\n "Provo, UT": "1",\n "Foresvik, Norway Portland, ND": "1",\n "London / Chicago, IL": "1",\n "Broomfield, Chelmsford, England": "1",\n "Syria Youngstown, OH": "1",\n "Asarum, Sweden Brooklyn, NY": "1",\n "Belgium Montreal, PQ": "1",\n "Springfield, MA": "1",\n "Guernsey / Wilmington, DE": "1",\n "New York, NY / Washington, DC": "1",\n "Co Clare, Ireland Washington, DC": "1",\n "Wimbledon Park, London / Hayling Island, Hants": "1",\n "Salo, Finland Astoria, OR": "1",\n "Mexico City, Mexico": "1",\n "Green Bay, WI": "1",\n "Cincinatti, OH": "1",\n "Streatham, Surrey": "1",\n "Stoughton, MA": "1",\n "St Anne\'s-on-Sea, Lancashire": "1",\n "Berne, Switzerland / Central City, IA": "1",\n "Oslo, Norway Bayonne, NJ": "1",\n "Folkstone, Kent / New York, NY": "1",\n "Bournemouth, England": "1",\n "Myren, Sweden New York, NY": "1",\n "Plymouth, Dorset / Houghton, MI": "1",\n "New York, NY / Briarcliff Manor NY": "1",\n "Illinois, USA": "1",\n "Pomeroy, WA": "1",\n "Pondersend, England / New Durham, NJ": "1",\n "Lake Arthur, Chavez County, NM": "1",\n "London / Middlesex": "1",\n "Kilmacowen, Co Sligo, Ireland New York, NY": "1",\n "Helsinki, Finland Ashtabula, Ohio": "1",\n "Tranvik, Finland New York": "1",\n "Sweden / Arlington, NJ": "1",\n "Bristol, England Cleveland, OH": "1",\n "Aughnacliff, Co Longford, Ireland New York, NY": "1",\n "Sarnia, ON": "1",\n "England / Philadelphia, PA": "1",\n "London Skanteales, NY": "1",\n "Glasgow": "1",\n "Oskarshamn, Sweden Minneapolis, MN": "1",\n "Gunnislake, England / Butte, MT": "1",\n "Norrlot, Sweden Chicago, IL": "1",\n "Windsor, England New York, NY": "1",\n "Finland Sudbury, ON": "1",\n "Bergen, Norway": "1",\n "Goteborg, Sweden / Rockford, IL": "1",\n "Kontiolahti, Finland / Detroit, MI": "1",\n "Hartford, Huntingdonshire": "1",\n "?Havana, Cuba": "1",\n "Co Limerick, Ireland Sherbrooke, PQ": "1",\n "Auburn, NY": "1",\n "Waukegan, Chicago, IL": "1",\n "Dagsas, Sweden Fower, MN": "1",\n "Antwerp, Belgium / Stanton, OH": "1",\n "Hessle, Yorks": "1",\n "Worcester, England": "1",\n "Norway Los Angeles, CA": "1",\n "Denmark / New York, NY": "1",\n "Belfast, NI": "1",\n "Chelsea, London": "1",\n "Syria Fredericksburg, VA": "1",\n "England New York, NY": "1",\n "Southsea, Hants": "1",\n "Bayside, Queens, NY": "1",\n "Denver, CO": "1",\n "Lower Clapton, Middlesex or Erdington, Birmingham": "1",\n "Sittingbourne, England / San Diego, CA": "1",\n "Cooperstown, NY": "1",\n "Sweden Joliet, IL": "1",\n "St Andrews, Guernsey": "1",\n "Hamilton, ON": "1",\n "Union Hill, NJ": "1",\n "Catford, Kent / Detroit, MI": "1",\n "Halesworth, England": "1",\n "New Britain, CT": "1",\n "Ireland Philadelphia, PA": "1",\n "London / Fort Byron, NY": "1",\n "Albany, NY": "1",\n "Co Cork, Ireland Roxbury, MA": "1",\n "Belmont, MA": "1",\n "Krakoryd, Sweden Bloomington, IL": "1",\n "Walthamstow, England": "1",\n "Paris, France New York, NY": "1",\n "Bennington, VT": "1",\n "Argentina": "1",\n "Finland / Washington, DC": "1",\n "Jacksonville, FL": "1",\n "Lima, Peru": "1",\n "Haddenfield, NJ": "1",\n "Oslo, Norway Cameron, WI": "1",\n "Toronto, ON": "1",\n "Frankfort, KY": "1",\n "Cologne, Germany": "1",\n "Surbiton Hill, Surrey": "1",\n "Stockholm, Sweden / Washington, DC": "1",\n "Cambridge, MA": "1",\n "London, England / Marietta, Ohio and Milwaukee, WI": "1",\n "St Ives, Cornwall / Houghton, MI": "1",\n "Co Cork, Ireland Charlestown, MA": "1",\n "Vadsbro, Sweden Ministee, MI": "1",\n "Sault St Marie, ON": "1",\n "Kingwilliamstown, Co Cork, Ireland New York, NY": "1",\n "Hartford, CT": "1",\n "Dorchester, MA": "1",\n "Bath, England / Massachusetts": "1",\n "Kingston, Surrey": "1",\n "Yoevil, England / Cottage Grove, OR": "1",\n "North Evington, England": "1",\n "Dorking, Surrey, England": "1",\n "Finland / Minneapolis, MN": "1",\n "Lyndhurst, England": "1",\n "Greensburg, PA": "1",\n "Austria Niagara Falls, NY": "1",\n "Brennes, Norway New York": "1",\n "Goteborg, Sweden Huntley, IL": "1",\n "India / Rapid City, SD": "1",\n "Russia": "1",\n "Kingwilliamstown, Co Cork, Ireland Glens Falls, NY": "1",\n "Harrow-on-the-Hill, Middlesex": "1",\n "Italy Philadelphia, PA": "1",\n "Tokyo, Japan": "1",\n "Bridgwater, Somerset, England": "1",\n "Devonport, England": "1",\n "Australia Fingal, ND": "1",\n "Brighton, MA": "1",\n "Buenos Aires, Argentina / New Jersey, NJ": "1",\n "India / Pittsburgh, PA": "1",\n "England / Detroit, MI": "1",\n "Bristol, Avon / Jacksonville, FL": "1",\n "Southampton / New York, NY": "1",\n "Isle of Wight, England": "1",\n "England / Hartford, CT": "1",\n "Cornwall, England Houghton, MI": "1",\n "Ruotsinphyhtaa, Finland New York, NY": "1",\n "Sweden Chicago, IL": "1",\n "Karberg, Sweden Jerome Junction, AZ": "1",\n "Copenhagen, Denmark": "1",\n "Sydney, Australia": "1",\n "Rotterdam, Netherlands": "1",\n "Germantown, Philadelphia, PA": "1",\n "Roachdale, IN": "1",\n "Upper Burma, India Pittsburgh, PA": "1",\n "Taalintehdas, Finland Hoboken, NJ": "1",\n "Harrow, England": "1",\n "Ireland Brooklyn, NY": "1",\n "London / Staten Island, NY": "1",\n "Stockholm, Sweden New York": "1",\n "Guernsey, England / Edgewood, RI": "1",\n "London / Birmingham": "1",\n "Birkdale, England Cleveland, Ohio": "1",\n "East Bridgewater, MA": "1",\n "Buffalo, NY": "1",\n "Manchester, England": "1",\n "Tofta, Sweden Joliet, IL": "1",\n "Medeltorp, Sweden Chicago, IL": "1",\n "Woodford County, KY": "1",\n "London / Winnipeg, MB": "1",\n "Elmira, NY / Orange, NJ": "1",\n "Ascot, Berkshire / Rochester, NY": "1",\n "Middleburg Heights, OH": "1",\n "London / Montreal, PQ": "1"\n },\n "distinct_count_with_nan": 370,\n "distinct_count_without_nan": 369,\n "type": "Variable.TYPE_CAT",\n "n": 1309,\n "count": 745,\n "n_distinct": 369,\n "p_distinct": 0.49530201342281877,\n "p_missing": 0.43086325439266615,\n "n_missing": 564,\n "is_unique": "False",\n "n_unique": 217,\n "p_unique": 0.2912751677852349,\n "memory_size": 10600,\n "top": "New York, NY",\n "freq": 64,\n "histogram_frequencies": [\n "[296 35 21 6 3 3 2 1 0 0 1 0 0 0 0 0 0 0\\n 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\\n 0 0 0 0 0 0 0 0 0 0 0 0 0 1]",\n "[ 1. 2.26 3.52 4.78 6.04 7.3 8.56 9.82 11.08 12.34 13.6 14.86\\n 16.12 17.38 18.64 19.9 21.16 22.42 23.68 24.94 26.2 27.46 28.72 29.98\\n 31.24 32.5 33.76 35.02 36.28 37.54 38.8 40.06 41.32 42.58 43.84 45.1\\n 46.36 47.62 48.88 50.14 51.4 52.66 53.92 55.18 56.44 57.7 58.96 60.22\\n 61.48 62.74 64. ]"\n ],\n "chi_squared": [\n 2339.245637583893,\n 3.035931785831085e-283\n ],\n "length": {\n "0": "12",\n "1": "31",\n "2": "31",\n "3": 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"[ZEROS] warning on column sibsp",\n "[ZEROS] warning on column parch",\n "[ZEROS] warning on column fare"\n ],\n "package": {\n "pandas_profiling_version": "2.9.0",\n "pandas_profiling_config": "title: Titanic Report\\ncorrelations:\\n cramers:\\n calculate: no\\n warn_high_correlations: yes\\n threshold: 0.9\\n phi_k:\\n calculate: no\\n warn_high_correlations: no\\n kendall:\\n calculate: no\\n warn_high_correlations: no\\n spearman:\\n calculate: no\\n warn_high_correlations: no\\n pearson:\\n calculate: no\\n warn_high_correlations: yes\\n threshold: 0.9\\n recoded:\\n calculate: no\\n warn_high_correlations: no\\n threshold: 0.0\\nmissing_diagrams:\\n dendrogram: no\\n heatmap: no\\n matrix: no\\n bar: no\\nmemory_deep: yes\\nn_freq_table_max: 25\\nn_extreme_obs: 10\\nn_obs_unique: 10\\nvars:\\n image:\\n active: yes\\n exif: yes\\n hash: yes\\n file:\\n active: yes\\n cat:\\n unicode: yes\\n length: yes\\n cardinality_threshold: 50\\n n_obs: 5\\n chi_squared_threshold: 0.999\\n coerce_str_to_date: no\\n redact: no\\n num:\\n quantiles:\\n - 0.05\\n - 0.25\\n - 0.5\\n - 0.75\\n - 0.95\\n skewness_threshold: 20\\n low_categorical_threshold: 5\\n chi_squared_threshold: 0.999\\n bool:\\n n_obs: 3\\ndataset:\\n description: \'\'\\n creator: \'\'\\n author: \'\'\\n copyright_holder: \'\'\\n copyright_year: \'\'\\n url: \'\'\\nvariables:\\n descriptions: {}\\nshow_variable_description: yes\\npool_size: 0\\nprogress_bar: yes\\nsort: None\\ninteractions:\\n targets: []\\n continuous: yes\\ncategorical_maximum_correlation_distinct: 100\\nplot:\\n image_format: svg\\n dpi: 800\\n scatter_threshold: 1000\\n correlation:\\n cmap: RdBu\\n bad: \'#000000\'\\n missing:\\n cmap: RdBu\\n force_labels: yes\\n pie:\\n max_unique: 10\\n histogram:\\n x_axis_labels: yes\\n bins: 50\\n max_bins: 250\\nduplicates:\\n head: 10\\nsamples:\\n head: 10\\n tail: 10\\nreject_variables: yes\\nnotebook:\\n iframe:\\n height: 800px\\n width: 100%\\n attribute: srcdoc\\nhtml:\\n minify_html: yes\\n use_local_assets: yes\\n inline: yes\\n navbar_show: yes\\n file_name: None\\n style:\\n theme: None\\n logo: \'\'\\n primary_color: \'#337ab7\'\\n full_width: no\\n"\n },\n "sample": [\n "Sample(id=\'head\', data= pclass survived name sex \\\\\\n0 1 1 Allen, Miss. Elisabeth Walton female \\n1 1 1 Allison, Master. Hudson Trevor male \\n2 1 0 Allison, Miss. Helen Loraine female \\n3 1 0 Allison, Mr. Hudson Joshua Creighton male \\n4 1 0 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female \\n5 1 1 Anderson, Mr. Harry male \\n6 1 1 Andrews, Miss. Kornelia Theodosia female \\n7 1 0 Andrews, Mr. Thomas Jr male \\n8 1 1 Appleton, Mrs. Edward Dale (Charlotte Lamson) female \\n9 1 0 Artagaveytia, Mr. Ramon male \\n\\n age sibsp parch fare cabin embarked body \\\\\\n0 29.0000 0 0 211.3375 B5 S NaN \\n1 0.9167 1 2 151.5500 C22 C26 S NaN \\n2 2.0000 1 2 151.5500 C22 C26 S NaN \\n3 30.0000 1 2 151.5500 C22 C26 S 135.0 \\n4 25.0000 1 2 151.5500 C22 C26 S NaN \\n5 48.0000 0 0 26.5500 E12 S NaN \\n6 63.0000 1 0 77.9583 D7 S NaN \\n7 39.0000 0 0 0.0000 A36 S NaN \\n8 53.0000 2 0 51.4792 C101 S NaN \\n9 71.0000 0 0 49.5042 NaN C 22.0 \\n\\n home.dest \\n0 St Louis, MO \\n1 Montreal, PQ / Chesterville, ON \\n2 Montreal, PQ / Chesterville, ON \\n3 Montreal, PQ / Chesterville, ON \\n4 Montreal, PQ / Chesterville, ON \\n5 New York, NY \\n6 Hudson, NY \\n7 Belfast, NI \\n8 Bayside, Queens, NY \\n9 Montevideo, Uruguay , name=\'First rows\', caption=None)",\n "Sample(id=\'tail\', data= pclass survived name sex age \\\\\\n1299 3 0 Yasbeck, Mr. Antoni male 27.0 \\n1300 3 1 Yasbeck, Mrs. Antoni (Selini Alexander) female 15.0 \\n1301 3 0 Youseff, Mr. Gerious male 45.5 \\n1302 3 0 Yousif, Mr. Wazli male NaN \\n1303 3 0 Yousseff, Mr. Gerious male NaN \\n1304 3 0 Zabour, Miss. Hileni female 14.5 \\n1305 3 0 Zabour, Miss. Thamine female NaN \\n1306 3 0 Zakarian, Mr. Mapriededer male 26.5 \\n1307 3 0 Zakarian, Mr. Ortin male 27.0 \\n1308 3 0 Zimmerman, Mr. Leo male 29.0 \\n\\n sibsp parch fare cabin embarked body home.dest \\n1299 1 0 14.4542 NaN C NaN NaN \\n1300 1 0 14.4542 NaN C NaN NaN \\n1301 0 0 7.2250 NaN C 312.0 NaN \\n1302 0 0 7.2250 NaN C NaN NaN \\n1303 0 0 14.4583 NaN C NaN NaN \\n1304 1 0 14.4542 NaN C 328.0 NaN \\n1305 1 0 14.4542 NaN C NaN NaN \\n1306 0 0 7.2250 NaN C 304.0 NaN \\n1307 0 0 7.2250 NaN C NaN NaN \\n1308 0 0 7.8750 NaN S NaN NaN , name=\'Last rows\', caption=None)"\n ],\n "duplicates": "Empty DataFrame\\nColumns: [pclass, survived, name, sex, age, sibsp, parch, fare, cabin, embarked, body, home.dest, count]\\nIndex: []"\n}'
Handling Larger Datasets¶
Generating the Pandas Profiling Report can take a large amount of time, especially with larger datasets. A report can be generated faster by setting the "minimal" arguement to True, as in the below example:
In [34]:
titanicReportMin = ProfileReport(titanicDF, title='Titanic Report Minimized', minimal = True)
titanicReportMin
Out[34]:
As you can see, this minimized version of the report only includes the Overview and Variables sections.
In [ ]: